Estimation techniques for deriving the Basel and IFRS 9 LGD estimates on retail bank portfolios
Abstract
A stable financial system is essential for growth in banks. A financial crisis can damage banks, as was seen in the financial crisis of 2008. Banks are subject to government regulation to reduce the risk of future financial crises. Amongst several requirements, capital requirements, as set out by local government, are influenced by the Bank for International Settlements’ Basel Committee on Banking
Supervision. The requirements, as set out in the Basel Accord, allow banks to build risk models for three risk drivers, namely the probability of default (PD), loss given default (LGD) and exposure at default (EAD). The risk drivers are combined to predict the unexpected credit loss that is used as a safety cushion against unexpected credit losses. Banks are also subject to financial reporting and
disclosure requirements. International Financial Reporting Standards (IFRS) are standards issued by the IFRS Foundation and the International Accounting Standards Board (IASB). The IFRS 9 standard gives guidance with regard to the estimation of impairments and typically the same three risk drivers are used. Impairments models are used to estimate provisions that banks need to hold against expected credit losses. The accuracy of these risk drivers are also key to the stability of banks. The objective of this thesis is to develop LGD models for Basel and IFRS 9 that adhere to the required regulations. LGD methodologies can be classified into direct and indirect methodologies. Under Basel, a direct and indirect LGD model was developed. The direct LGD model was adapted for IFRS 9 requirements. Survival analysis is one of the approaches used in direct LGD modelling. A standard method in this approach is the EAD weighted survival analysis (denoted by EWSA). The first article will aim to enhance the survival analysis estimation of LGD. Firstly by using default weighted LGD estimates and incorporating negative cashflows and secondly by catering for over recoveries. We will denote this new method to predict LGD as the default weighted survival analysis (DWSA). These enhancements were motivated by the fact that the South African Reserve Bank requires banks to use default weighted LGD estimates in regulatory capital calculations. Therefore, by including this into the survival analysis approach, the model is aligned more closely to regulations. Recovery datasets used by banks include both negative and over recoveries. By including these into the LGD estimation, the models are more closely aligned to the actual data. The assumption is that the predictive power of the model should therefore be improved by adding these changes. The proposed model is tested on eight datasets. Three of these are actual retail bank datasets and five are simulated. The datasets used are epresentative of the data typically used in LGD estimations in the South African retail environment. When the indirect LGD methodology is used, two components exist, namely the loss severity component and the probability component. Commonly used models to respectively predict the loss severity and the probability component are the haircut- and the logistic regression models. In the second article, survival analysis is proposed as an improvement to the more traditional logistic regression method. By testing the MSE (mean squared error), bias and variance of the two methodologies, it was shown that the improvement enhanced the model’s predictive power. The proposed LGD methodology (using survival analysis) was applied on two simulated datasets and two retail bank datasets, and outperformed the logistic regression LGD methodology. Additional benefits included that the new methodology could allow for censoring as well as predicting probabilities over varying outcome periods. The third article is aimed at adapting the DWSA method, used in the first article to model the Basel LGD to estimate the LGD for IFRS 9 impairment requirements. The DWSA methodology allows for over recoveries, default weighting and negative cashflows. This IFRS 9 LGD is used in the calculation of the expected credit losses (ECL) as per the IFRS 9 standard. The IFRS 9 LGD methodology that is described in this paper makes use of survival analysis to estimate the LGD. The
Cox proportional hazards model allows that a baseline survival curve can be adjusted to produce survival curves for different segments of the portfolio. The forward-looking LGD values are adjusted for different macro-economic scenarios and an ECL is calculated for each scenario. These ECL values are probability-weighted to produce a single ECL number. This paper illustrates the IFRS 9 LGD as well as the ECL on a real dataset from a retail portfolio of a South African bank.